J G S
1 2 6 0 0
Journal Code
Manuscript No.
-
Dispatch: 6.11.13
CE: Sathya
No. of pages: 5
PE: Saranya Shree
BRIEF REPORTS
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
Depressive Symptoms Precede Memory Decline, but Not Vice
Versa, in Non-Demented Older Adults
Laura B. Zahodne, PhD, Yaakov Stern, PhD, and Jennifer J. Manly, PhD
Key words: depression; episodic memory; statistical
modeling
OBJECTIVES: To determine whether depressive symptoms typically precede or follow memory declines.
DESIGN: An autoregressive latent trajectory model was
used to examine the direction of the relationship between
depressive symptoms and memory decline observed over
12 years.
SETTING: Washington/Hamilton Heights Inwood Columbia Aging Project, a community-based longitudinal study
of aging and dementia in northern Manhattan.
PARTICIPANTS: Older adults initially without dementia
(N = 2,425).
MEASUREMENTS: Memory composite scores were computed from three subscores of the Selective Reminding
Test. Depressive symptoms were assessed using a 10-item
version of the Center for Epidemiologic Studies Depression
Scale. Analyses controlled for age, sex, recruitment wave,
education, black race, and Hispanic ethnicity measured at
baseline and chronic disease burden measured at each
study visit.
RESULTS: Initial depressive symptoms predicted worse
memory scores at the second study visit (B
weight = 0.03; P = .003) and accelerated memory
decline over the entire study period (B weight = 0.02;
P = .03). Memory scores did not predict subsequent
depressive symptoms.
CONCLUSION: These findings suggest that depressive
symptoms precede memory decline, but not vice versa, in
late life. This pattern of results is in line with hypotheses
that depression is a prodrome of dementia or a causal contributor to memory decline. Clinicians should be aware
that depressive symptoms may represent an early indicator
not only of dementia, as reported previously, but also of
memory decline more generally. J Am Geriatr Soc 2013.
From the Cognitive Neuroscience Division, Department of Neurology,
Taub Institute for Research on Alzheimer’s Disease and the Aging Brain,
College of Physicians and Surgeons, Columbia University, New York,
New York.
Address correspondence to Laura B. Zahodne, Sergievsky Center/Taub
Institute, Columbia University, 630 West 168th Street, P & S Box 16,
New York, NY 10032. E-mail: [email protected]
DOI: 10.1111/jgs.12600
JAGS 2013
© 2013, Copyright the Authors
Journal compilation © 2013, The American Geriatrics Society
R
1
ecent meta-analyses have demonstrated that depression is a major risk factor for mild cognitive impairment (MCI) and dementia.1,2 Depressive symptoms also
increased dementia risk in the Washington/Hamilton
Heights Inwood Columbia Aging Project (WHICAP),3 but
because depressive symptoms did not predict greater risk
of MCI in cognitively normal older adults, it was concluded that depression accompanies, but does not precede,
cognitive impairment, but this study was not explicitly
designed to test for leading and lagging relationships
between depressive symptoms and cognitive impairment
that would clearly demonstrate which occurs first. The
present study sought to elucidate the temporal ordering of
depressive symptoms and memory decline in WHICAP.
The question of whether depressive symptoms precede
the development of memory impairment is of theoretical
and practical importance. Several hypotheses have been
proposed. Depression may reflect a psychological reaction
to the perception of memory decline.4 Memory dysfunction may be a risk factor for late-onset psychiatric illness.5
Depression may be a prodrome of dementia.6 Depression
may be a causal contributor to memory decline.7 Depression may lower the threshold for clinical detection of
dementia without directly influencing brain pathology.8
Some evoke a reciprocal relationship.9 Each hypothesis
provides specific predictions regarding the directionality of
the relationship between depressive symptoms and memory
decline. These predictions can be tested only in a longitudinal framework, allowing for estimation of both potential
outcomes simultaneously.
The current study used the autoregressive latent trajectory (ALT) framework to test the direction of the relationship between depressive symptoms and memory decline in
older adults. ALT is a structural equation model that combines two well-developed approaches: multivariate latent
growth curve modeling and autoregressive cross-lagged
panel analysis.10 ALT tests whether the initial level of one
0002-8614/13/$15.00
2
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
2013
ZAHODNE ET AL.
outcome influences the subsequent trajectory of another
outcome while simultaneously testing for lagged relationships at individual occasions. The current study examined
whether depressive symptoms precede memory impairment
or vice versa over the course of 12 years in a sample of
2,425 older adults initially without dementia.
METHODS
Participants and Procedures
The 2,425 older adults were participants in WHICAP, a
prospective, community-based longitudinal study of aging
and dementia in a racially and ethnically diverse sample.
Study procedures have been previously described.11,12 Participants within the geographic area of northern Manhattan were identified from Medicare records and recruited in
two waves: 1992 (N = 478) and 1999 (N = 1,947). Ongoing follow-up at 18- to 24-month intervals includes a battery of cognitive, functional, and health measures
administered in the participant’s preferred language (English or Spanish). This study complies with the ethical rules
for human experimentation stated in the Declaration of
Helsinki, including approval of the local institutional
review board and informed consent. Race and ethnicity
are determined through self-report using the format of the
2000 U.S. Census. Baseline characteristics of the sample
are shown in Table 1.
Because the depression instrument used in these analyses was not included in the original WHICAP battery, the
first visit was defined as the first at which each participant
was administered this instrument. The current sample
included only participants who did not meet Diagnostic
and Statistical Manual of Mental Disorders, Third
Table 1. Sample Characteristics at Baseline
Characteristic
Age, mean SD (range)
Education (years), mean SD (range)
Female, %
Black, %
Hispanic, %
Illness burden, mean SD (range)a
CESD, mean SD (range)
Memory composite score,
mean SD (range)b
Number of CESD assessments,
mean SD (range)c
Number of memory assessment,
mean SD (range)d
Value
77.3 10.3 67.2
32.2
37.7
2.4 1.8 0.0 6.6 (65.8–101.8)
4.8 (0–20)
1.5 (0–8)
2.0 (0–10)
0.8 ( 3.3 to 2.2)
2.7 1.2 (0–4)
2.6 1.2 (0–4)
a
One point was assigned for the presence of each chronic condition listed
in the text (range 0–15).
b
Memory composite scores are averaged z-scores from immediate recall,
delayed recall, and delayed recognition scores from the Selective Reminding Test.
c
Number of assessments during the study period; three individuals did
not complete the Centers for Epidemiologic Studies Depression Scale
(CES-D; range 0–10) but had memory testing.
d
Number of assessments during the study period; 24 individuals had no
memory testing but had been administered the CES-D.
SD = standard deviation.
JAGS
Edition, criteria for dementia at this first visit, according
to consensus conference. Four visits (follow-up of
12 years) were used in the current analyses to maximize
covariance coverage.
Outcomes
Episodic memory was assessed using the Selective Reminding Test (SRT),13 which was chosen because of its sensitivity to longitudinal change and dementia conversion in this
population. Participants are given six trials to learn 12
words. After each trial, participants are reminded only of
words they failed to recall. Total learning is the number of
words recalled after six learning trials. Delayed recall is
the number of words recalled after a 15-minute delay.
Delayed recognition is the number of words recognized
immediately after delayed recall. Total learning, delayed
recall, and delayed recognition scores at each occasion
were standardized to a z-score metric using the sample’s
means and standard deviations (SDs) at the initial occasion. Memory composite scores were computed by averaging the three z-scores at each occasion. Higher scores
indicate better memory.
Depressive symptoms were assessed using a 10-item
version of the Center for Epidemiologic Studies Depression
Scale (CES-D).14 Participants self-report responses to 10
dichotomous items. Total scores range from 0 to 10, with
higher scores indicating worse depression.
Statistical Analysis
Descriptive statistics were computed using SPSS version 19
(IBM Corp., Armonk, NY). Growth curve and ALT analyses were conducted in Mplus version 7 (Muthen &
Muthen, Los Angeles, CA) using maximum likelihood estimation. Time was parameterized as years from study
entry, accommodating differing intervals between visits.
The average number of years between visits was
2.6 0.9. Missing data were managed using full information maximum likelihood (FIML), which accumulates and
maximizes case-wise likelihood functions computed using
all available data for each participant. FIML produces lessbiased estimates than alternative methods.15 FIML does
not assume that data are missing completely at random
and can therefore accommodate missingness related to previous scores. This feature of FIML is desirable because
participants lost to follow-up are often those who scored
lower at earlier occasions.
First, unconditional latent growth curve models
(LGCs) were estimated separately for the two outcomes
(episodic memory and depressive symptoms) to characterize their trajectories. Models allowing only linear change
were statistically compared with models allowing linear
and quadratic change using the chi-square (v2) test.
Next, best-fitting univariate LGCs were combined into
the ALT model shown in Figure 1. In the latent part of
the model, initial levels (intercepts), rates of change (linear
slopes), and changes in rates of change (quadratic slopes)
were estimated as latent variables using information from
all four occasions. To determine whether initial levels of
depressive symptoms and memory impairment were
uniquely associated with subsequent changes in either
JAGS
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
2013
DEPRESSIVE SYMPTOMS AND MEMORY DECLINE
3
age 77, male sex, recruitment in 1992, and 10 years of
education. Finally, a time-varying covariate reflecting overall illness burden was allowed to correlate with depressive
symptoms and memory scores at each occasion. One point
each was assigned for the presence of heart disease, hypertension, stroke, diabetes mellitus, pulmonary disease, thyroid disease, liver disease, renal insufficiency, peptic ulcer
disease, peripheral vascular disease, cancer, Parkinson’s
disease, Essential tremor, multiple sclerosis, and arthritis.
Points were summed to reflect illness burden at each occasion as previously described.16,17 All reported associations
from the ALT model are independent of all covariates.
RESULTS
Unconditional Univariate Models
Characterizing the Trajectories
Trajectories of change for each outcome were characterized using unconditional univariate growth models. Fit
was better in models allowing for curvilinear change in
memory (∆v2(4) = 64.03, P < .001) and depressive
symptoms (∆v2(4) = 35.55, P < .001) than in models
allowing for only linear change. Therefore, models estimating both linear and quadratic slopes were retained.
Descriptions of the Trajectories in the Best-Fitting
Univariate Models
Figure 1. Schematic of the autoregressive latent trajectory
model. For simplicity, covariates are not shown. Values represent unstandardized B weights for the cross-lagged paths,
adjusted for age, education, recruitment year, race, ethnicity,
and illness burden, followed by standard errors in parentheses. As shown, all associations between the latent variables
(intercepts and slopes) were estimated. Lower initial memory
score (memory intercept) and higher initial level of depressive
symptoms (depression intercept) were each independently
associated with faster subsequent memory decline (memory
linear slope). aP = .003.
domain over the entire study period, rates of change in
each symptom type were regressed on initial levels of each
symptom type. All other relationships between latent variables were estimated as correlations.
In the autoregressive cross-lagged part of the model,
within-domain autoregressive paths, cross-lagged regression paths, and occasion-specific correlations across
domains were estimated. Type 1 error was controlled by
applying a Bonferroni correction to these parameter estimates (0.05/16 = 0.003125). Cross-lagged paths tested
whether depressive symptoms at one study visit predicted
memory scores at the next visit and vice versa.
The ALT model also controlled for covariates. Associations between age, sex, recruitment year, education, black
race, and Hispanic ethnicity, measured at baseline, and the
latent variables for each outcome were estimated. These
time-invariant covariates were centered to facilitate parameter interpretation. Specifically, values of 0 correspond to
In these unconditional univariate models, memory scores
declined by 0.13 points per year (P < .001). This rate of
change decelerated by 0.01 points per year (P < .001). Participants with higher initial memory scores exhibited
slower subsequent decline (covariance = 0.04, P = .003).
Scores on the CES-D improved by 0.12 points per year
(P < .001). This rate of change decelerated by 0.02 points
per year (P < .001). Initial level of depressive symptoms
was not related to subsequent changes (covariance = 0.21, P = .08).
Autoregressive Latent Trajectory Model
Best-fitting univariate models were combined, and all covariates were added, as shown in Figure 1. All reported associations were independent of all other associations in the
model, including covariate effects.
Associations Between the Latent Variables
Initial level of depressive symptoms was not associated
with subsequent changes in depressive symptoms
(P = .22). Initial level of depressive symptoms was not
associated with initial memory score (covariance = 0.06;
P = .35). Rates of change in depressive symptoms and
memory were not correlated (covariance = 0.01; P = .52).
Higher initial memory scores were associated with
slower subsequent memory decline (B = 0.07; P = .01).
Independent of this relationship, higher initial level of
depressive symptoms was associated with accelerated
memory decline (B = 0.02; beta = 0.26; P = .03). Specifically, each additional depressive symptom was associated with 0.02 more points of annual decline on the
4
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
2013
ZAHODNE ET AL.
memory composite scores, independent of initial memory
score and all covariates. Initial memory score was not
associated with subsequent changes in depressive symptoms (B = 0.06; beta = 0.15; P = .08). This pattern of
results was unchanged after exclusion of individuals with
MCI at baseline.12
Autoregressive and Cross-Lagged Paths
No within-domain autoregressive paths were significant.
Cross-lagged paths are shown in Figure 1. Memory
scores did not predict depressive symptoms at the next
visit. Greater depressive symptoms at the first visit significantly predicted worse memory scores at the second
visit (P = .003) after Bonferroni correction. No other
cross-lagged paths were significant. This pattern of
results was unchanged after excluding individuals with
MCI at baseline.
Covariate Effects
Effects of the time-invariant covariates are shown in
Table 2. Greater illness burden was associated with more
depressive symptoms at each occasion (all P < .001). Illness burden was not associated with memory scores at any
occasion.
DISCUSSION
Depressive symptoms preceded memory decline in this
sample of 2,425 older adults initially without dementia
followed up to 12 years. Specifically, higher scores on the
depression measure at the first study visit predicted lower
scores on a memory composite at the next study visit. Furthermore, higher initial level of depression predicted faster
rate of memory decline measured over the entire study period. This is the first study to directly examine the temporal
ordering of depressive symptoms and memory impairment
in older adults using the ALT framework. These results
have implications for memory prognosis and theories
regarding how depressive symptoms relate to memory
decline in late life.
These findings add to those of a recent study that used
survival analyses to reveal an association between depressive symptoms and greater dementia risk in this sample.3
Table 2. Effects of the Time-Invariant Covariates
Measured at Baseline
Depressive
Symptoms
Covariate
Age
Female sex
Black
Hispanic
Education, years
Recruitment yeara
a
1992 or 1999.
P < b.05, c.001.
Initial
Level
0.008
0.530c
0.498c
0.262b
0.015
0.177
Rate of
Change
0.002
0.021
0.022
0.071
0.001
0.115
Memory
Initial
Level
0.037c
0.254c
0.308c
0.300c
0.049c
0.109b
Rate of
Change
0.007b
0.024
0.029
0.048
0.003
0.109c
JAGS
That study did not find an association between baseline
depressive symptoms and conversion from a normal cognitive state to MCI. In contrast, the present study found that
depressive symptoms predicted steeper cognitive decline
even in individuals who were cognitive normal at baseline.
It is likely that these discrepancies relate to the earlier
study’s focus on conversion rather than the rate of cognitive decline and the dichotomization of the CES-D deemphasizing subsyndromal depressive symptoms.
Results are in line with two prominent hypotheses
regarding the nature of the relationship between depressive
symptoms and memory impairment in late life. Specifically,
depression may be a prodrome of dementia6 or a causal
contributor to memory decline.7 The current finding that
memory scores did not predict subsequent changes on a
depression instrument do not support hypotheses that
depression reflects a psychological reaction to the perception of memory decline4 or that memory dysfunction is a
risk factor for late-onset depression,5 although neither participants’ perceptions of memory impairment nor formal
psychiatric diagnoses were analyzed in this study. In addition, the results of this large-scale study do not imply that
such relationships do not exist for individual cases.
The finding that depressive symptoms predicted
worse subsequent memory decline was independent of
age, sex, education, race, ethnicity, recruitment year, and
illness burden, including vascular disease. It has been suggested that vascular health may mediate the link between
depression and dementia risk,18 but epidemiological data
do not strongly support such a link.19 Furthermore, a
previous report from our group showed that vascular risk
factors and stroke did not explain the prospective relationship between depressive symptoms and Alzheimer’s
disease (AD).20
If depression represents a prodrome of dementia, one
might predict an association between depressive symptoms
and dementia neuropathology. Worse AD pathology (e.g.,
plaques and tangles) has been reported in individuals with
AD with a history of major depression,21 although recent
studies have found more subcortical Lewy bodies, hippocampal neuronal loss, and white matter lesions, but not
AD pathology, in the brains of older adults with depression without dementia.22,23
If depression is a causal contributor to dementia, one
might expect depressive symptoms to predict subsequent
changes in brain integrity. For example, depression may
cause hippocampal damage through a glucocorticoid cascade.24 A recent longitudinal study of 334 older adults
with mild cognitive impairment (MCI) found that depressive symptoms predicted accelerated cortical thinning in
the prefrontal cortex, where glucocorticoid receptors are
highly expressed,25 but it is also possible that a separate
pathology (e.g., hippocampal sclerosis) causes depressive
symptoms and memory loss in older adults.
The present results complement and clarify those from
a recent study that used ALT to show reciprocal relationships between depressive symptoms and disability in community-dwelling adults in Taiwan.26 That study concluded
that disability is a stronger predictor of depressive symptoms than vice versa. Together, these studies support
depression as a negative prognostic indicator for cognition
and disability, although the lack of a relationship between
JAGS
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
2013
memory and subsequent depressive symptoms in the present study suggests that it is unlikely that memory decline
mediates the previously described link between disability
and changes in depressive symptoms.26 Rather, physical
changes that limit the performance of activities of daily living are more likely to lead to depressive symptoms than is
memory decline. A future study that includes measures of
depression, memory, and disability is needed to test this
hypothesis directly.
Strengths of this study include its large sample size,
long follow-up period, and statistical framework. Limitations include the brief measure of depressive symptoms
and the use of a single memory measure. Although the
association between initial memory performance and rate
of change in depressive symptoms was not significant in
this study of 2,425 older adults followed over 12 years
(P = .08), it is possible that this association could be significant in a larger study. In addition, depressive symptoms
are less common in this and other community-based samples than in clinic-based samples. Future studies should
determine whether these findings differ in clinic-based samples and investigate whether temporal relationships
between depressive symptoms and cognitive status differ
for different cognitive domains.
CONCLUSION
Depressive symptoms preceded memory decline, but not
vice versa, in this sample of older adults initially without
dementia followed for 12 years. Clinicians should be
aware that depressive symptoms may represent an early
indicator not only of dementia, as reported previously, but
also of memory decline more generally. This pattern of
results is in line with hypotheses that depression is a prodrome of dementia or a causal contributor to memory
decline.
ACKNOWLEDGMENTS
Conflict of Interest: The editor in chief has reviewed the
conflict of interest checklist provided by the authors and
has determined that the authors have no financial or any
other kind of personal conflicts with this paper.
Funding Disclosure: This work was supported by the
National Institute on Aging (Grants AG037212,
AG034189, AG000261).The content is solely the responsibility of the authors and does not necessarily represent the
official views of the National Institutes of Health.
Author Contributions: Zahodne: Conception and
design, analysis and interpretation of data, drafting the
article, final approval of the version to be published. Stern
and Manly: Conception and design, interpretation of data,
critical revision, final approval of the version to be published.
Sponsor’s Role: The sponsors had no role in the study
design, methods, subject recruitment, data collection,
analysis, preparation of article, or decision to submit the
article for publication.
DEPRESSIVE SYMPTOMS AND MEMORY DECLINE
5
REFERENCES
1. Gao Y, Huang C, Zhao K et al. Depression as a risk factor for dementia
and mild cognitive impairment: A meta-analysis of longitudinal studies. Int
J Geriatr Psychiatry 2013;28:441–449.
2. Diniz BS, Butters MA, Albert SM et al. Late-life depression and risk of vascular dementia and Alzheimer’s disease: Systematic review and meta-analysis of community-based cohort studies. Br J Psychiatry 2013;202:329–335.
3. Richard E, Reitz C, Honig LH et al. Late-life depression, mild cognitive
impairment, and dementia. JAMA Neurol 2013;70:374–382.
4. Bassuk SS, Berkman LF, Wypij D. Depressive symptomatology and incident
cognitive decline in an elderly community sample. Arch Gen Psychiatry
1998;55:1073–1081.
5. Teng E, Tassniyam K, Lu PH. Reduced quality of life ratings in mild cognitive impairment: Analyses of subject and informant responses. Am J Geriatr
Psychiatry 2010;20:1016–1025.
6. Reding M, Haycox J, Blass J. Depression in patients referred to a dementia
clinic. Arch Neurol 1985;42:894–896.
7. Meeks TW, Ropacki SA, Jeste DV. The neurobiology of neuropsychiatric
symptoms in dementia. Curr Opin Psychiatry 2006;19:581–586.
8. Mortimer JA. Do psychosocial risk factors contribute to Alzheimer’s disease? In: Henderson AS, Henderson JH, eds. Etiology of Dementia of Alzheimer’s Type. Chichester, UK: Wiley. 1988, pp 39–52.
9. O’Hara R. The reciprocal relationship of neurocognitive and neuropsychiatric function in late life. Am J Geriatr Psychiatry 2010;20:1001–1005.
10. Bollen KA, Curran PJ. Autoregressive latent trajectory (ALT) models: A
synthesis of two traditions. Sociol Method Res 2004;32:336–383.
11. Tang MX, Cross P, Andrews H et al. Incidence of AD in African-Americans, Caribbean Hispanics, and Caucasians in northern Manhattan. Neurology 2001;56:49–56.
12. Manly JJ, Bell-McGinty S, Tang MX et al. Implementing diagnostic criteria
and estimating frequency of mild cognitive impairment in an urban community. Arch Neurol 2005;62:1739–1746.
13. Buschke H, Fuld PA. Evaluating storage, retention, and retrieval in disordered memory and learning. Neurology 1974;24:1019–1025.
14. Irwin M, Haydari K, Oxman MN. Screening for depression in the older
adult: Criterion validity of the 10-item Center for Epidemiological Studies
Depression Scale (CES-D). Arch Intern Med 1999;159:1701–1704.
15. Enders CK, Bandalos DL. The relative performance of full information
maximum likelihood estimation for missing data in structural equation
models. Struct Equ Modeling 2001;8:430–457.
16. Grunebaum MF, Oquendo MA, Manly JJ. Depressive symptoms and antidepressant use in a random community sample of ethnically diverse, urban
elder persons. J Affect Disord 2008;105:273–277.
17. Manly JJ, Schupf N, Stern Y et al. Telephone-based identification of MCI
and dementia in a multicultural cohort. Arch Neurol 2011;68:607–614.
18. Alexopoulos GS, Meyers BS, Young RC et al. Clinically defined vascular
depression. Am J Psychiatry 1997;154:562–565.
19. Panza F, Frisardi V, Capurso C et al. Late-life depression, mild cognitive
impairment, and dementia: Possible continuum? Am J Geriatr Psychiatry
2010;18:98–116.
20. Luchsinger JA, Honig LS, Tang M-X et al. Depressive symptoms, vascular
risk factors, and Alzheimer’s disease. Int J Geriatr Psychiatry 2008;23:922–
928.
21. Rapp MA, Schnaider-Beeri M, Grossman HT et al. Increased hippocampal
plaques and tangles in patients with Alzheimer’s disease with a lifetime history of major depression. Arch Gen Psychiatry 2006;63:161–167.
22. Tsopelas C, Stewart R, Savva GM et al. Neuropathological correlates of
late-life depression in older people. Br J Psychiatry 2011;198:109–114.
23. Madsen K, Hasselbalch BJ, Frederiksen KS et al. Lack of association
between prior depressive episodes and cerebral [11C]PiB binding. Neurobiol Aging 2012;33:2334–2342.
24. Sapolsky RM. Depression, antidepressants, and the shrinking hippocampus.
Proc Natl Acad Sci USA 2001;98:12320–12322.
25. Zahodne LB, Gongvatana A, Cohen R et al. Are apathy and depression
independently associated with longitudinal trajectories of cortical atrophy
in mild cognitive impairment? Am J Geriatr Psychiatry 2013;pii:S1064–
2
S7481.
26. Chen C-M, Mullan J, Su Y-Y et al. The longitudinal relationship between
depressive symptoms and disability for older adults: A population-based
study. J Gerontol A Biol Sci Med Sci 2010;67A:1059–1067.
Author Query Form
Journal:
Article:
JGS
12600
Dear Author,
During the copy-editing of your paper, the following queries arose. Please respond to these by marking up your
proofs with the necessary changes/additions. Please write your answers on the query sheet if there is insufficient
space on the page proofs. Please write clearly and follow the conventions shown on the attached corrections sheet.
If returning the proof by fax do not write too close to the paper’s edge. Please remember that illegible mark-ups
may delay publication.
Many thanks for your assistance.
Query reference
Query
1
AUTHOR: Please check that authors and their affiliations is correct.
2
AUTHOR: Please check volume number in reference [25].
Remarks
USING e-ANNOTATION TOOLS FOR ELECTRONIC PROOF CORRECTION
Required software to e-Annotate PDFs: Adobe Acrobat Professional or Adobe Reader (version 8.0 or
above). (Note that this document uses screenshots from Adobe Reader X)
The latest version of Acrobat Reader can be downloaded for free at: http://get.adobe.com/reader/
Once you have Acrobat Reader open on your computer, click on the Comment tab at the right of the toolbar:
This will open up a panel down the right side of the document. The majority of
tools you will use for annotating your proof will be in the Annotations section,
rkevwtgf"qrrqukvg0"YgÓxg"rkemgf"qwv"uqog"qh"vjgug"vqqnu"dgnqy<
1. Replace (Ins) Tool Î for replacing text.
2. Strikethrough (Del) Tool Î for deleting text.
Strikes a line through text and opens up a text
box where replacement text can be entered.
How to use it
Strikes a red line through text that is to be
deleted.
How to use it
‚
Highlight a word or sentence.
‚
Highlight a word or sentence.
‚
Click on the Replace (Ins) icon in the Annotations
section.
‚
Click on the Strikethrough (Del) icon in the
Annotations section.
‚
Type the replacement text into the blue box that
appears.
3. Add note to text Tool Î for highlighting a section
to be changed to bold or italic.
4. Add sticky note Tool Î for making notes at
specific points in the text.
Highlights text in yellow and opens up a text
box where comments can be entered.
How to use it
Marks a point in the proof where a comment
needs to be highlighted.
How to use it
‚
Highlight the relevant section of text.
‚
‚
Click on the Add note to text icon in the
Annotations section.
Click on the Add sticky note icon in the
Annotations section.
‚
Click at the point in the proof where the comment
should be inserted.
‚
Type the comment into the yellow box that
appears.
‚
Type instruction on what should be changed
regarding the text into the yellow box that
appears.
USING e-ANNOTATION TOOLS FOR ELECTRONIC PROOF CORRECTION
5. Attach File Tool Î for inserting large amounts of
text or replacement figures.
6. Add stamp Tool Î for approving a proof if no
corrections are required.
Inserts an icon linking to the attached file in the
appropriate pace in the text.
How to use it
Inserts a selected stamp onto an appropriate
place in the proof.
How to use it
‚
Click on the Attach File icon in the Annotations
section.
‚
Click on the Add stamp icon in the Annotations
section.
‚
Enkem"qp"vjg"rtqqh"vq"yjgtg"{qwÓf"nkmg"vjg"cvvcejgf"
file to be linked.
‚
‚
Select the file to be attached from your computer
or network.
Select the stamp you want to use. (The Approved
stamp is usually available directly in the menu that
appears).
‚
Enkem"qp"vjg"rtqqh"yjgtg"{qwÓf"nkmg"vjg"uvcor"vq"
appear. (Where a proof is to be approved as it is,
this would normally be on the first page).
‚
Select the colour and type of icon that will appear
in the proof. Click OK.
7. Drawing Markups Tools Î for drawing shapes, lines and freeform
annotations on proofs and commenting on these marks.
Allows shapes, lines and freeform annotations to be drawn on proofs and for
comment to be made on these marks..
How to use it
‚
Click on one of the shapes in the Drawing
Markups section.
‚
Click on the proof at the relevant point and
draw the selected shape with the cursor.
‚
To add a comment to the drawn shape,
move the cursor over the shape until an
arrowhead appears.
‚
Double click on the shape and type any
text in the red box that appears.
For further information on how to annotate proofs, click on the Help menu to reveal a list of further options: